


from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

__UpperCamelCase     		=							TypeVar('T')







class 							lowerCamelCase__						(				Generic[T]   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							None

						UpperCamelCase__       =							len(snake_case  )
						UpperCamelCase__       =							[any_type for _ in range(self.N  )] + arr
						UpperCamelCase__       =							fnc
						self.build()




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for p in range(self.N - 1   ,    0   ,    -1  ):
									UpperCamelCase__       =							self.fn(self.st[p * 2]   ,    self.st[p * 2 + 1]  )




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						p += self.N
						UpperCamelCase__       =							v
						while p > 1:
									UpperCamelCase__       =							p // 2
									UpperCamelCase__       =							self.fn(self.st[p * 2]   ,    self.st[p * 2 + 1]  )







			def 					snake_case__					(				self   ,    snake_case   ,    snake_case  ):  # noqa: E741

						'''simple docstring'''


						UpperCamelCase__,   UpperCamelCase__       =							l + self.N, r + self.N

						UpperCamelCase__       =							None
						while l <= r:
									if l % 2 == 1:
												UpperCamelCase__       =							self.st[l] if res is None else self.fn(snake_case   ,    self.st[l]  )
									if r % 2 == 0:
												UpperCamelCase__       =							self.st[r] if res is None else self.fn(snake_case   ,    self.st[r]  )
									UpperCamelCase__,   UpperCamelCase__       =							(l + 1) // 2, (r - 1) // 2
						return res


if __name__ == "__main__":
						from functools import reduce

						__UpperCamelCase     		=							[1, 1_0, -2, 9, -3, 8, 4, -7, 5, 6, 1_1, -1_2]

						__UpperCamelCase     		=							{
						    0: 7,
						    1: 2,
						    2: 6,
						    3: -1_4,
						    4: 5,
						    5: 4,
						    6: 7,
						    7: -1_0,
						    8: 9,
						    9: 1_0,
						    1_0: 1_2,
						    1_1: 1,
						}

						__UpperCamelCase     		=							SegmentTree(test_array, min)
						__UpperCamelCase     		=							SegmentTree(test_array, max)
						__UpperCamelCase     		=							SegmentTree(test_array, lambda a, b: a + b)
						def 							UpperCamelCase_(					)->		None:
									for i in range(len(_A	)	):
												for j in range(_A					,  len(_A	)	):
															UpperCamelCase__       =							reduce(_A					,  test_array[i : j + 1]	)
															UpperCamelCase__       =							reduce(_A					,  test_array[i : j + 1]	)
															UpperCamelCase__       =							reduce(lambda _A					,  _A	: a + b					,  test_array[i : j + 1]	)
															assert min_range == min_segment_tree.query(_A					,  _A	)
															assert max_range == max_segment_tree.query(_A					,  _A	)
															assert sum_range == sum_segment_tree.query(_A					,  _A	)

						test_all_segments()

						for index, value in test_updates.items():
												__UpperCamelCase     		=							value
												min_segment_tree.update(index, value)
												max_segment_tree.update(index, value)
												sum_segment_tree.update(index, value)
												test_all_segments()






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase     		=							{
    'configuration_xlm_roberta_xl': [
        'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XLMRobertaXLConfig',
        'XLMRobertaXLOnnxConfig',
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase     		=							[
						    'XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'XLMRobertaXLForCausalLM',
						    'XLMRobertaXLForMaskedLM',
						    'XLMRobertaXLForMultipleChoice',
						    'XLMRobertaXLForQuestionAnswering',
						    'XLMRobertaXLForSequenceClassification',
						    'XLMRobertaXLForTokenClassification',
						    'XLMRobertaXLModel',
						    'XLMRobertaXLPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_xlm_roberta_xl import (
						    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    XLMRobertaXLConfig,
						    XLMRobertaXLOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_xlm_roberta_xl import (
												    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
												    XLMRobertaXLForCausalLM,
												    XLMRobertaXLForMaskedLM,
												    XLMRobertaXLForMultipleChoice,
												    XLMRobertaXLForQuestionAnswering,
												    XLMRobertaXLForSequenceClassification,
												    XLMRobertaXLForTokenClassification,
												    XLMRobertaXLModel,
												    XLMRobertaXLPreTrainedModel,
												)

else:
						import sys

						__UpperCamelCase     		=							_LazyModule(__name__, globals()['__file__'], _import_structure)


